R/summarySEwithin.R

In mssm-msf-2019/BiostatsALL:

#' ## Summarizes data, handling within-subjects variables by removing inter-subject variability.
#' @description It will still work if there are no within-S variables. Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%). If there are within-subject variables, calculate adjusted values using method from Morey (2008).
#' @param    data: a data frame.
#' @param   measurevar: the name of a column that contains the variable to be summariezed
#' @param   betweenvars: a vector containing names of columns that are between-subjects variables
#' @param   withinvars: a vector containing names of columns that are within-subjects variables
#' @param   idvar: the name of a column that identifies each subject (or matched subjects)
#' @param   na.rm: a boolean that indicates whether to ignore NA's
#' @param   conf.interval: the percent range of the confidence interval (default is 95%)
#' 
summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
                            idvar=NULL, na.rm=FALSE, conf.interval=.95, .drop=TRUE) {
  
  # Ensure that the betweenvars and withinvars are factors
  factorvars <- sapply(data[, c(betweenvars, withinvars), drop=FALSE], FUN=is.factor)
  if (!all(factorvars)) {
    nonfactorvars <- names(factorvars)[!factorvars]
    message("Automatically converting the following non-factors to factors: ",
            paste(nonfactorvars, collapse = ", "))
    data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
  }
  
  # Norm each subject's data    
  data <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)
  
  # This is the name of the new column
  measureNormedVar <- paste(measurevar, "Normed", sep="")
  
  # Replace the original data column with the normed one
  data[,measurevar] <- data[,measureNormedVar]
  
  # Collapse the normed data - now we can treat between and within vars the same
  datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars), na.rm=na.rm,
                     conf.interval=conf.interval, .drop=.drop)
  
  # Apply correction from Morey (2008) to the standard error and confidence interval
  #  Get the product of the number of conditions of within-S variables
  nWithinGroups    <- prod(sapply(datac[,withinvars, drop=FALSE], FUN=nlevels))
  correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
  
  # Apply the correction factor
  datac$sd <- datac$sd * correctionFactor
  datac$se <- datac$se * correctionFactor
  datac$ci <- datac$ci * correctionFactor
  
  return(datac)
}